What should an AI strategy include? It should include ten connected parts: business outcomes, a priority use-case portfolio, an AI readiness baseline, data and technology direction, responsible AI principles, governance, an operating model, a workforce plan, an investment roadmap and a system for measuring value.
A list is not enough. Each part must record a decision, the evidence behind it, a named owner and a way to judge progress. That is what turns ambition into a strategy leaders can actually use.
In this article
- The 10 essential parts of an AI strategy
- How the strategy should be documented
- A practical executive test
- Frequently asked questions
“A complete AI strategy connects ambition to operating reality. It explains not only where the organization wants to go, but also who will decide, what must be built, and how value will be proved.”
What should an AI strategy include? The 10 essential parts
1. Strategic outcomes
Start with the business results AI is expected to improve. Examples include shorter service time, fewer errors, stronger forecasting or better customer retention. Give each outcome a baseline, target, time frame and accountable executive. “Adopt AI” is not a business outcome.
2. A prioritised use-case portfolio
Describe where AI will be used, who will use it and which process will change. Compare opportunities using business value, feasibility, cost, data needs, risk and time to benefit. A portfolio view prevents the loudest idea from winning by default. It also shows which proposals should wait or stop.
3. An evidence-based readiness baseline
Assess data, technology, skills, governance, culture and delivery capacity before promising results. Readiness is not one score. A company may have strong infrastructure but weak data ownership, or keen employees but no safe procurement route. Our guide to assessing AI readiness explains these dimensions.
4. Data and technology direction
Set principles for data quality, access, privacy, security, architecture, integration and model sourcing. Decide what the organization will buy, build or obtain through a partner. Avoid naming a platform before the use cases and constraints are clear. The technology must support the strategy, not define it.
5. Responsible AI principles
State the standards that apply to every AI use. The updated OECD AI Principles cover human rights, transparency, robustness, security, safety and accountability. An organization should translate these ideas into plain rules, including when people must be told about AI and when human review is required.
6. Governance and risk controls
Define decision rights, risk levels, approvals, documentation, monitoring, incident response and retirement. Controls should match possible harm. The NIST AI Risk Management Framework organizes this work through Govern, Map, Measure and Manage. ISO/IEC 42001:2023 adds a management-system approach for ongoing policies, objectives and improvement.
7. An AI operating model
Explain how business units, technology, data, risk, legal, privacy, security and human resources will work together. Name who owns demand, delivery, standards and post-launch performance. The model may be central, distributed or hybrid. What matters is that authority and hand-offs are visible.
8. A workforce and change plan
Identify affected roles, workflow changes, required skills and employee concerns. Training should use real tasks and continue after launch. Managers need support too, since measures, quality checks and job design may change. AI adoption is a work redesign issue as much as a software issue.
9. An investment and delivery roadmap
Sequence initiatives by dependency and value. Show funding, owners, milestones, decision gates and scale criteria. Do not turn the roadmap into a fixed three-year promise. AI tools and risks move quickly, so leaders need regular points to stop, adjust or expand the work. See how to build an AI transformation roadmap.
10. Value measurement
Choose measures before implementation. Track the business outcome, adoption, quality, risk and full cost, not just model accuracy. Record who owns each benefit and how often results will be reviewed. A use case should scale only when evidence supports the next investment.
How should an AI strategy be documented?
Keep the main strategy short enough for executives to use. A practical package often has three layers:
- Executive strategy: ambition, choices, priority outcomes, boundaries and ownership.
- Portfolio and roadmap: use cases, sequencing, funding, dependencies and decision gates.
- Operating appendices: governance, standards, risk methods, data requirements and measures.
Every major section should answer four questions: What was decided? Why? Who owns it? How will progress be judged? A 70-page report that avoids these questions is not stronger than a clear 12-page strategy.
A practical executive test
Ask the leadership team to explain the strategy without using a vendor or model name. Then ask which use cases will not be funded, what evidence would stop a pilot and who remains accountable after launch. If the answers are vague, the organization has more design work to do.
Leaders should also check that the strategy connects to the wider business direction. Our article on aligning AI with business strategy provides a direct method for doing this.
Frequently asked questions
Who should own the AI strategy?
Executive leadership should own the business choices. A CIO, CDO or AI leader may coordinate the work, but business leaders must remain accountable for outcomes in their areas.
How often should it be reviewed?
Review the portfolio and risks at least quarterly during active delivery. Revisit the full strategy after a major business change, regulatory shift or change in AI capability.
Does a small organization need all 10 parts?
Yes, but not at the same level of detail as a large enterprise. A small firm may capture the ten parts in a concise document with simple controls. The decisions still need to be clear.
Executive takeaway
What should an AI strategy include? Enough detail to guide investment, work design, governance and measurement, but no more than leaders can use. Connect the ten parts. Name the owners. Put evidence behind every scale decision.
Praevion Consulting Inc.’s management consulting services help leadership teams assess readiness, select AI priorities and build practical strategies.
To discuss your organization, contact Praevion Consulting Inc.
References
- National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework 1.0. NIST AI 100-1, January 2023.
- International Organization for Standardization. ISO/IEC 42001:2023, Artificial intelligence management systems. December 2023.
- OECD. OECD AI Principles. Adopted 2019 and updated 2024.
- Innovation, Science and Economic Development Canada. Canada’s National Artificial Intelligence Strategy: AI for All. 2026.
